Unstructured Grid Visualization Cookbook
Overview
If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,
This is a numbered list of the specific topics
These should map approximately to your main sections of content
Or each second-level,
##, header in your notebookKeep the size and scope of your notebook in check
And be sure to let the reader know up front the important concepts they’ll be leaving with
Prerequisites
This section was inspired by this template of the wonderful The Turing Way Jupyter Book.
Following your overview, tell your reader what concepts, packages, or other background information they’ll need before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with | vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.
Label the importance of each concept explicitly as helpful/necessary.
Concepts |
Importance |
Notes |
|---|---|---|
Necessary |
||
Helpful |
Familiarity with metadata structure |
|
Project management |
Helpful |
Time to learn: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.
System requirements:
Populate with any system, version, or non-Python software requirements if necessary
Otherwise use the concepts table above and the Imports section below to describe required packages as necessary
If no extra requirements, remove the System requirements point altogether
Imports
Begin your body of content with another --- divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports up-front:
# Organization and data handling
import numpy as np
import pandas as pd
import uxarray as ux
import xarray as xr
import time
import requests
# General Plotting
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# Plotting with matplotlib
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
# Plotting with datashader
import holoviews as hv
import hvplot.pandas
import geoviews.feature as gf
Dataset
# Load data files from web
ds_file_480 = requests.get("https://web.lcrc.anl.gov/public/e3sm/inputdata/share/meshes/mpas/ocean/oQU480.230422.nc").content
ds_file_120 = requests.get("https://web.lcrc.anl.gov/public/e3sm/inputdata/share/meshes/mpas/ocean/oQU120.230424.nc").content
# Open datasets from files
ds_480 = xr.open_dataset(ds_file_480)
ds_120 = xr.open_dataset(ds_file_120)
ds_480
<xarray.Dataset>
Dimensions: (nEdges: 5754, maxEdges2: 12, TWO: 2,
nVertices: 3947, vertexDegree: 3, nCells: 1791,
nVertLevels: 60, maxEdges: 6, Time: 1)
Dimensions without coordinates: nEdges, maxEdges2, TWO, nVertices,
vertexDegree, nCells, nVertLevels, maxEdges,
Time
Data variables: (12/49)
edgesOnEdge (nEdges, maxEdges2) int32 ...
weightsOnEdge (nEdges, maxEdges2) float64 ...
cellsOnEdge (nEdges, TWO) int32 ...
verticesOnEdge (nEdges, TWO) int32 ...
angleEdge (nEdges) float64 ...
dcEdge (nEdges) float64 ...
... ...
refTopDepth (nVertLevels) float64 ...
refZMid (nVertLevels) float64 ...
refLayerThickness (nVertLevels) float64 ...
layerThickness (Time, nCells, nVertLevels) float64 ...
ssh (Time, nCells) float64 ...
zMid (Time, nCells, nVertLevels) float64 ...
Attributes: (12/14)
model_name: mpas
core_name: ocean
source: MPAS
Conventions: MPAS
git_version: v4.0-630-gaa1be43
on_a_sphere: YES
... ...
x_period: 0.0
y_period: 0.0
history: Sat Apr 22 16:39:35 2023: /gpfs/fs1/home/ac.xylar/chrysal...
parent_id: lsd2k42w1r\na4x6sqhkwb
mesh_spec: 0.0
file_id: kufh9o2jwxds_120
<xarray.Dataset>
Dimensions: (nEdges: 87980, maxEdges2: 12, TWO: 2,
nVertices: 59329, vertexDegree: 3,
nCells: 28571, nVertLevels: 100, maxEdges: 6,
Time: 1)
Dimensions without coordinates: nEdges, maxEdges2, TWO, nVertices,
vertexDegree, nCells, nVertLevels, maxEdges,
Time
Data variables: (12/49)
edgesOnEdge (nEdges, maxEdges2) int32 ...
weightsOnEdge (nEdges, maxEdges2) float64 ...
cellsOnEdge (nEdges, TWO) int32 ...
verticesOnEdge (nEdges, TWO) int32 ...
angleEdge (nEdges) float64 ...
dcEdge (nEdges) float64 ...
... ...
refTopDepth (nVertLevels) float64 ...
refZMid (nVertLevels) float64 ...
refLayerThickness (nVertLevels) float64 ...
layerThickness (Time, nCells, nVertLevels) float64 ...
ssh (Time, nCells) float64 ...
zMid (Time, nCells, nVertLevels) float64 ...
Attributes: (12/15)
model_name: mpas
core_name: ocean
source: MPAS
Conventions: MPAS
git_version: v4.0-973-g678b0d3
on_a_sphere: YES
... ...
y_period: 0.0
history: Mon Apr 24 15:11:05 2023: /gpfs/fs1/home/ac.xylar/chrysal...
parent_id: a3ix7sajjh\nfo1xwy6fbp\ne513bmrqm3\n
mesh_spec: 0.0
file_id: f5z41zseqm
NCO: 4.0.5Working with Unstructured Grids using UXarray
Grid Representation
uxds_480 = ux.open_dataset(ds_file_480, ds_file_480)
uxds_480.uxgrid
<uxarray.Grid>
Original Grid Type: mpas
Grid Dimensions:
* nMesh2_node: 3947
* nMesh2_face: 1791
* nMaxMesh2_face_nodes: 6
* nMesh2_edge: 5754
Grid Coordinate Variables:
* Mesh2_node_x: (3947,)
* Mesh2_node_y: (3947,)
* Mesh2_face_x: (1791,)
* Mesh2_face_y: (1791,)
Grid Connectivity Variables:
* Mesh2_face_nodes: (1791, 6)
* Mesh2_edge_nodes: (5754, 2)
* nNodes_per_face: (1791,)
uxds_120 = ux.open_dataset(ds_file_120, ds_file_120)
uxds_120.uxgrid
<uxarray.Grid>
Original Grid Type: mpas
Grid Dimensions:
* nMesh2_node: 59329
* nMesh2_face: 28571
* nMaxMesh2_face_nodes: 6
* nMesh2_edge: 87980
Grid Coordinate Variables:
* Mesh2_node_x: (59329,)
* Mesh2_node_y: (59329,)
* Mesh2_face_x: (28571,)
* Mesh2_face_y: (28571,)
Grid Connectivity Variables:
* Mesh2_face_nodes: (28571, 6)
* Mesh2_edge_nodes: (87980, 2)
* nNodes_per_face: (28571,)
Plotting with HoloViz
Conversion to GeoDataFrame
gdf_480_grid = uxds_480.uxgrid.to_geodataframe()
gdf_120_grid = uxds_120.uxgrid.to_geodataframe()
Nodes
hv.extension("matplotlib")
plot_kwargs = {"size": 5.0, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
"coastline": True, "width": 800}
hv.Layout(gdf_480_grid.hvplot.points(**plot_kwargs) + gdf_120_grid.hvplot.points(**plot_kwargs)).opts(fig_size=150)
/usr/share/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
hv.extension("bokeh")
plot_kwargs = {"s": 1.0, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
"coastline": True}
hv.Layout(gdf_480_grid.hvplot.points(**plot_kwargs) + gdf_120_grid.hvplot.points(**plot_kwargs))
Edges
hv.extension("matplotlib")
plot_kwargs = {"linewidth": 0.5, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
"coastline": True, "width": 1600}
hv.Layout(gdf_480_grid.hvplot.paths(**plot_kwargs) + gdf_120_grid.hvplot.paths(**plot_kwargs)).opts(fig_size=150)
hv.extension("bokeh")
plot_kwargs = {"line_width": 0.5, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
"coastline": True}
hv.Layout(gdf_480_grid.hvplot.paths(**plot_kwargs) + gdf_120_grid.hvplot.paths(**plot_kwargs))
